In many communications and computing systems, multiple applications share resources. Each of these applications requires access to other software and hardware resources of the communications and computing system. It is important that the sharing of these resources be done in a way that will fulfill the overall goals of the users and administrators of the system. Applications must have access to the resources of the system in a manner that is congruent with both the capacity of the system and the quality of services required by the application. In case of shortages of resources, more important applications should be given access to resources at the expense of less important applications so that more important functions can be maintained.
Typically, each application of a system is independent of others and the requirements of each application will vary from moment to moment. The sharing of resources of the system cannot be strategically fixed since the applications and the relative priority of these applications are all highly dynamic.
In a real time environment, it is necessary that real time applications get access to needed resources within the time constraints required of their tasks.
Real time systems can be characterized as those that must supply services that meet both absolute and relative time constraints. Real time systems are not necessarily those where the time requirements are short. A successful real time system is one that can meet the time guarantees for the applications using them. The time requirements may be short or long. It is desirable that a real time system be able to set and adjust the timing commitments for the services that it can provide to applications and monitor compliance, taking remedial action if necessary. Real time operation is necessary for the performance of a service. It is also necessary to make run time decisions for allocations of resources.
In some cases, real time constraints are considered to be inflexible or hard. Hard real time constraints are system requirements whose violation would lead to the defeat of the system purpose and possibly severe system instability. It is desirable that a system be able to adjust priorities to ensure that hard time constraints are met.
There is always a specific set of resources available to any system. Of necessity, this set of resources is finite. Applications compete with each other for access to resources. Coordination and allocation mechanisms need to be put in place which partition these resources to applications in a way which optimizes the performance to meet the overall system needs.
In the past, it has been common to use a mechanism for resource allocation that employs an economic model. Each application is allocated or initialized with a description of the necessary resources, including capacity and type of resource it requires, as well as an amount of economic utility which acts as a form of money with which to obtain or “buy” the resources. Applications enter a negotiation phase among themselves in which they buy and sell resources or access to resources to meet their needs. This economic model produces a rationality which can collapse the complicated logic of the relative importance of applications and allows for the sharing of resources in an attempt to minimize the expenditure of utility.
Applications using the economic model for resource allocation are structured to conserve the amount of utility that they expend. The mechanism is self-regulating. High priority applications are given more utility. They can use this in negotiation to outbid less important applications for the resources they need. In addition, applications can share resources and expend their utility across multiple resources. Applications must expend a higher amount of utility to obtain higher demand resources.
In a traditional communication or software system, the system must be designed to be reliable and manageable so that it will behave in a predictable manner and properly function. However, as systems become large and complex, they become difficult to understand, maintain and modify. As systems become more dynamic, understanding their temporal characteristics becomes more difficult, and therefore understanding and correlating the overall system behavior is very difficult and time consuming. Hence, in such complex systems, an agent approach has frequently been adapted to simplify the design and management of these complex communications and computing systems. An agent based approach means that the overall problem can be partitioned into a number of smaller, simpler components which are easier to develop, maintain, manage and supplement. Agent based solutions frequently provide a natural means of modeling a problem so that real world entities and their interactions can be mapped into autonomous problem solving agents which have their own resources and expertise and can interact with others to get the tasks done.
The concept of a general system using agents has been described in the publications “Toward A Taxonomy of Multi-Agent Systems”, Int. J. Man-Machine Studies (1993), 39, 689-704, Academic Press Limited, and “An Intelligent Agent Framework for Enterprise Integration” by Jeff Y. C. Pan and Jay M. Tenenbaum, Transactions on Systems, Man and Cybernetics, (Vol. 21, No. 6, November/December, 1991, pages 1391-1407.)
Mihai Barbuceanu and Mark S. Fox, (“Integrating Communicative Action, Conversation and Decision Theory in a Coordination Language for Multi-Agent System”, (1996) University of Toronto) have disclosed a language and design for providing objects and control structures to substantiate the construction of real multi-agent systems in industrial domains where agents communicate using structured conversations.
Tuomas Sandholm and Victor Lesser (“Issues in Automated Negotiations and Electronic Commerce; Extending the Contract Net Framework” in proceedings of the First International Conference on Multi-Agent Systems (ICMAS-95), pages 328-335, San Francisco) have discussed issues that arise in automated negotiation among self interested agents and have described a negotiation protocol and numerous message formats for negotiation of a contract between two agents.
The Spawn system (described in IEEE Transactions on Software Engineering, Vol. 18, No. 2, February, 1992, pages 103-117), discloses a computational system which is organized as a market economy composed of interacting buyers and sellers. The spawn system allows processes in an operating system to purchase the CPU time they need.
Michael P. Wellman, in the paper entitled “A computational Market Model for Distributed Configuration Design”, Proceedings of the Twelfth National Conference on Artificial Intelligence”; Jul. 24, 1994 discloses a negotiation system that allows distributed systems to configure themselves out of a catalog of resources. However, none of the systems described above disclose an agent based negotiation system that is tailored for real time systems. In both the Spawn and Wellman systems, a purchaser buys the use of a resource. This results in the purchaser owning rights to a resource. In the case of real time systems, this strategy can have difficulties. A real time application, when it needs a resource, will typically not have sufficient time to negotiate for its service requirements. The level of service must be guaranteed beforehand. There is a commercial need for a mechanism for negotiating a guarantee of service rather than the direct ownership shown in the previous models.